Abstract: Facilitating implementation of communication network deployment through network planning in advanced networks (e.g., 5G, 6G, and beyond) is provided herein. Operations of a system can include, configuring a first deployment scenario for first network equipment and a second deployment scenario for second network equipment. The first deployment scenario is selected from a group of first deployment scenarios and can include a first parameter. The second deployment scenario is selected from a group of second deployment scenarios and can include a second parameter. The configuring can include determining that a sum of the first parameter and the second parameter satisfies a function of a defined parameter level. The operations also can include facilitating a first enactment of the first deployment scenario for the first network equipment and a second enactment of the second deployment scenario for the second network equipment.

Abstract: Aspects of the subject disclosure may include, for example, obtaining first information indicative of data plane utilization, wherein the data plane is associated with a wireless communications network; obtaining second information indicative of control plane utilization, wherein the control plane is associated with the wireless communications network; applying the first information and the second information to one or more machine learning algorithms; and generating via the one or more machine learning algorithms one or more outputs, wherein the one or more outputs indicates whether one or more network resources should be added to improve the data plane utilization. Other embodiments are disclosed.

Abstract: Aspects of the subject disclosure may include, for example, receiving information about a data flow for radio communication between the radio access network and user equipment, classifying the data flow as one of a large data flow and a small data flow, adjusting priority of the data flow by reducing relative priority of the data flow responsive to classifying the data flow as a large data flow, and communicating data including the data flow between the radio access network and the user equipment according to the adjusted priority. Other embodiments are disclosed.

Abstract: Intelligent, automated, fixed wireless internet planning (e.g., using a computerized tool) is enabled. For instance, a system can comprise a processor and a memory that stores executable instructions that, when executed by the processor, facilitate performance of operations, comprising: determining, for a user equipment determined to be within a defined coverage area, a signal to interference and noise ratio, based on the signal to interference and noise ratio, determining a spectral efficiency value corresponding to the user equipment, based on the spectral efficiency value and a total available bandwidth of a network via which the defined coverage area is enabled, determining an available throughput corresponding to the user equipment, and in response to a determination that the available throughput exceeds a threshold throughput, designating the user equipment, in a data store, as being covered within the defined coverage area by the total available bandwidth of the network.

Abstract: A processing system including at least one processor may obtain operational data from a radio access network (RAN), format the operational data into state information and reward information for a reinforcement learning agent (RLA), processing the state information and the reward information via the RLA, where the RLA comprises a plurality of sub-agents, each comprising a respective neural network, each of the neural networks encoding a respective policy for selecting at least one setting of at least one parameter of the RAN to increase a respective predicted reward in accordance with the state information, and where each neural network is updated in accordance with the reward information. The processing system may further determine settings for parameters of the RAN via the RLA, where the RLA determines the settings in accordance with selections for the settings via the plurality of sub-agents, and apply the plurality of settings to the RAN.

Abstract: Intelligent, adaptive scheduling weight adjustment is enabled, e.g., to improve network performance. For instance, A non-transitory machine-readable medium can comprise executable instructions that, when executed by a processor, facilitate performance of operations, comprising based on key performance indicators corresponding to data traffic flows via a network, determining quality of service data representative of respective qualities of service for the data traffic flows, using a scheduling weight data traffic model generated using machine learning and trained using past quality of service data representative of past qualities of service of past data traffic flows via the network, from prior to the data traffic flows, and past scheduling weight settings applied to the past data traffic flows, determining a scheduling weight setting to be applied to a data traffic flow of the data traffic flows, and applying the scheduling weight setting to the data traffic flow.

Abstract: Intelligent, automated, fixed wireless internet planning (e.g., using a computerized tool) is enabled. For instance, a system can comprise a processor and a memory that stores executable instructions that, when executed by the processor, facilitate performance of operations, comprising: determining, for a user equipment determined to be within a defined coverage area, a signal to interference and noise ratio, based on the signal to interference and noise ratio, determining a spectral efficiency value corresponding to the user equipment, based on the spectral efficiency value and a total available bandwidth of a network via which the defined coverage area is enabled, determining an available throughput corresponding to the user equipment, and in response to a determination that the available throughput exceeds a threshold throughput, designating the user equipment, in a data store, as being covered within the defined coverage area by the total available bandwidth of the network.

Abstract: User quality of experience assessment in radio access networks is provided herein. A method can include measuring an average packet size of incoming data packets received via a radio access network, the incoming data packets respectively comprising data directed to respective network equipment served via the radio access network; determining service metrics for outgoing data packets transmitted via the radio access network to the respective network equipment in response to the incoming data packets being received via the radio access network, wherein the service metrics comprise an average transmission delay for the outgoing data packets and a packet loss rate for the outgoing data packets; and determining a quality of experience value associated with a performance of the radio access network based on a function of the average packet size of the incoming data packets and the service metrics for the outgoing data packets.

Abstract: Aspects of the subject disclosure may include, for example, receiving information about a data flow for radio communication between the radio access network and user equipment, classifying the data flow as one of a large data flow and a small data flow, adjusting priority of the data flow by reducing relative priority of the data flow responsive to classifying the data flow as a large data flow, and communicating data including the data flow between the radio access network and the user equipment according to the adjusted priority. Other embodiments are disclosed.

Abstract: Aspects of the subject disclosure may include, for example, calculating a respective first quality metric for each cell of a plurality of cells included in a network, calculating a respective second quality metric for each cell of the plurality of cells, calculating a capacity of each cell of the plurality of cells in accordance with the first quality metric for the cell and the second quality metric for the cell, and allocating traffic of the network amongst the plurality of cells in accordance with the respective capacity of each cell of the plurality of cells. Other embodiments are disclosed.

Abstract: Distribution of traffic to cells in a communication network can be controlled. A distribution management component (DMC) can determine overall device traffic throughput for cells of a sector that satisfy a defined traffic throughput criterion relating to a harmonic mean of the device traffic throughput for the cells to desirably enhance or maximize the harmonic mean of the overall device traffic throughput. Based on the overall device traffic throughput for the cells, the DMC can determine whether to adjust a characteristic associated with a cell of the cells to facilitate adjusting distribution of device traffic among the cells of the sector to achieve desirable load balancing of traffic by the sector and in the network. Load balancing can be achieved by controlling respective parameters with regard to communication devices that are in idle mode or connected mode to facilitate directing communication devices and associated traffic to desired cells.

Abstract: Facilitating implementation of communication network deployment through network planning in advanced networks (e.g., 5G, 6G, and beyond) is provided herein. Operations of a system can include, configuring a first deployment scenario for first network equipment and a second deployment scenario for second network equipment. The first deployment scenario is selected from a group of first deployment scenarios and can include a first parameter. The second deployment scenario is selected from a group of second deployment scenarios and can include a second parameter. The configuring can include determining that a sum of the first parameter and the second parameter satisfies a function of a defined parameter level. The operations also can include facilitating a first enactment of the first deployment scenario for the first network equipment and a second enactment of the second deployment scenario for the second network equipment.

Abstract: A processing system including at least one processor may obtain operational data from a radio access network (RAN), format the operational data into state information and reward information for a reinforcement learning agent (RLA), processing the state information and the reward information via the RLA, where the RLA comprises a plurality of sub-agents, each comprising a respective neural network, each of the neural networks encoding a respective policy for selecting at least one setting of at least one parameter of the RAN to increase a respective predicted reward in accordance with the state information, and where each neural network is updated in accordance with the reward information. The processing system may further determine settings for parameters of the RAN via the RLA, where the RLA determines the settings in accordance with selections for the settings via the plurality of sub-agents, and apply the plurality of settings to the RAN.

Abstract: The disclosed technology is directed towards load balancing in an adaptive and automated way for wireless mobility networks to improve the overall harmonic-average UE throughput within each controlled group of cells (e.g., different frequency carriers serving a sector of a base station). A load balancer (e.g., analytics component) obtains various device traffic data including throughput data for cells of a group. Pairs of cells in a group (sharing a site and face) can be selected based on satisfying various criteria, with estimated throughput gain achieved by changing the handoff rates between the cell pairs. The technology iteratively repeats the overall process, driving a system to an optimal equilibrium.

Abstract: Aspects of the subject disclosure may include, for example, calculating a respective first quality metric for each cell of a plurality of cells included in a network, calculating a respective second quality metric for each cell of the plurality of cells, calculating a capacity of each cell of the plurality of cells in accordance with the first quality metric for the cell and the second quality metric for the cell, and allocating traffic of the network amongst the plurality of cells in accordance with the respective capacity of each cell of the plurality of cells. Other embodiments are disclosed.

Abstract: The disclosed technology is directed towards load balancing in an adaptive and automated way for wireless mobility networks to improve the overall harmonic-average UE throughput within each controlled group of cells (e.g., different frequency carriers serving a sector of a base station). A load balancer (e.g., analytics component) obtains various device traffic data including throughput data for cells of a group. Pairs of cells in a group (sharing a site and face) can be selected based on satisfying various criteria, with estimated throughput gain achieved by changing the handoff rates between the cell pairs. The technology iteratively repeats the overall process, driving a system to an optimal equilibrium.

Abstract: Aspects of the subject disclosure may include, for example, receiving information about a data flow for radio communication between the radio access network and user equipment, classifying the data flow as one of a large data flow and a small data flow, adjusting priority of the data flow by reducing relative priority of the data flow responsive to classifying the data flow as a large data flow, and communicating data including the data flow between the radio access network and the user equipment according to the adjusted priority. Other embodiments are disclosed.

Abstract: The disclosed technology is directed towards load balancing in an adaptive and automated way for wireless mobility networks to improve the overall harmonic-average UE throughput within each controlled group of cells (e.g., different frequency carriers serving a sector of a base station). A load balancer (e.g., analytics component) obtains various device traffic data including throughput data for cells of a group. Pairs of cells in a group (sharing a site and face) can be selected based on satisfying various criteria, with estimated throughput gain achieved by changing the handoff rates between the cell pairs. The technology iteratively repeats the overall process, driving a system to an optimal equilibrium.

Abstract: The disclosed technology describes on-demand adjusting of the quality of service (QoS) class identifier (QCI) relative weight settings for different QCI classes associated with user devices. A QoS controller, which can be implemented in a standalone or a cloud configuration, updates the QCI weight settings as requested or needed to deal with changing network environments, such as growing traffic, different RF conditions, new devices, ratio of different service classes of user devices, to improve the overall performance of one or more cell sites. In one implementation, the QoS controller collects actual network statistics, and runs simulations based on those statistics with different groups of candidate QCI weight settings to generate multiple result sets. The QoS controller evaluates the result sets to determine which group of QCI weight settings meets desired performance objectives, and then applies those QCI weight settings to one or more NodeBs for use with actual data traffic.

Abstract: Aspects of the subject disclosure may include, for example, calculating a throughput of each cell of a plurality of cells of a communication network, calculating a total throughput of the plurality of cells, and distributing, in accordance with a first distribution, carrier aggregated traffic amongst the plurality of cells, wherein each cell obtains a respective portion of the carrier aggregated traffic as part of the first distribution in accordance with the throughput of the cell and the total throughput. Other embodiments are disclosed.